How to Track Model and Prediction Lineage in Vertex AI?
You are developing a process for training and running your custom model in production. You need to be able to show lineage for your model and predictions. What should you do?
Community Votes
53% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The question tests knowledge of Vertex AI lineage tracking capabilities, with the common trap being the confusion between Experiments (for hyperparameter tuning) and Pipelines (for artifact/prediction lineage).
Vertex AI Pipelines provide built-in artifact lineage tracking for model training and batch predictions using Vertex ML Metadata. The exam community strongly favors pipelines over experiments for end-to-end traceability in production workflows.
Option D (Vertex AI Experiments) is frequently chosen because it tracks training runs and metrics, but it lacks comprehensive artifact lineage for predictions and production pipeline components that Pipelines offer.
Community Discussion (18 comments)
guilhermebutzke
👍 8
Selected: D
My Answer: D According with: https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments “Vertex AI Experiments is a tool that helps you track and analyze different model architectures, hyperparameters, and training environments, letting you track the steps, inputs, and outputs of an experiment run. Vertex AI Experiments can also evaluate how your model performed in aggregate, against test datasets, and during the training run. You can then use this information to select the best model for your particular use case.”. Considering that both options A and B could demonstrate some form of lineage, I believe option D is the most suitable. The text explicitly states "show lineage for your model and predictions," which aligns perfectly with the functionality provided by Vertex AI Experiments.
edoo
👍 6
Selected: B
Vertex AI Pipelines are suited to do artifact lineage https://cloud.google.com/vertex-ai/docs/pipelines/lineage Experiments can do it also, but their main goal is to "track and analyze different model architectures, hyperparameters, and training environments"
bc3f222
👍 1
Selected: D
Vertex AI Experiments helps track all your training runs, including: Dataset version Hyperparameters Model metrics Code version This enables full lineage and traceability from data → training → model artifact.
Ankit267
👍 1
Selected: B
Answer is B D is wrong as there is only one model not models, experiments is used for multiple runs of a model/multiple models, also lineage is tracked using a pipeline
rajshiv
👍 1
Selected: B
Vertex AI Pipelines will track the Model lineage while the batch prediction component in Vertex AI Pipelines will provide lineage tracking because each prediction is part of the pipeline and is connected to the corresponding training process.
AB_C
👍 1
Selected: B
Vertex AI Pipeline for lineage tracking
Foxy2021
👍 1
My answer is B.
baimus
👍 1
It's a bit ambiguously worded this question. Model lineage involves knowledge of the data it was trained on, so that should be A. That being said, I think the question is implying D from it's wording, experiment tracking. I went for A, but suspect it's wrong.
SahandJ
👍 2
Selected: D
Option A/B doesn't mention anything about lineage. C is definitely wrong as there is no need to upload the dataset to Bigquery. Only correct answer is D
pinimichele01
👍 1
Selected: B
running your custom model in production -> need pipeline -> B
cruise93
👍 2
Selected: D
Agree with guilhermebutzke
Shark0
👍 1
Selected: A
A because to track lineage you need a managed dataset and vertex ai pipelines
Yan_X
👍 1
Selected: A
A D cannot provide lineage for the source of your data. Has to be A to go with Vertex AI managed dataset.
sonicclasps
👍 1
Selected: A
Managed data set to help track lineage https://cloud.google.com/vertex-ai/docs/training/using-managed-datasets
ddogg
👍 5
Selected: B
B) REF https://cloud.google.com/vertex-ai/docs/pipelines/lineage Track the lineage of pipeline artifacts When you run a pipeline using Vertex AI Pipelines, the artifacts and parameters of your pipeline run are stored using Vertex ML Metadata. Vertex ML Metadata makes it easier to analyze the lineage of your pipeline's artifacts, by saving you the difficulty of keeping track of your pipeline's metadata. An artifact's lineage includes all the factors that contributed to its creation, as well as artifacts and metadata that are derived from this artifact. For example, a model's lineage could include the following: The training, test, and evaluation data used to create the model. The hyperparameters used during model training. Metadata recorded from the training and evaluation process, such as the model's accuracy. Artifacts that descend from this model, such as the results of batch predictions.
b1a8fae
👍 1
Selected: D
D. Sample on how to keep track of experiments lineage -> https://cloud.google.com/vertex-ai/docs/experiments/user-journey/uj-model-training
BlehMaks
👍 1
Selected: B
Vertex AI Pipelines provides ability to track the lineage for your model and predictions
36bdc1e
👍 1
D “track the lineage of pipeline artifacts”. Vertex AI Experiments2 is a service that allows you to track and compare the results of your model training runs. Vertex AI Experiments automatically logs metadata such as hyperparameters, metrics, and artifacts for each training run.
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Expert Analysis
Why the Answer Is Correct
Vertex AI Pipelines automatically record artifact lineage through Vertex ML Metadata, capturing inputs, parameters, models, and outputs like predictions. When you run a pipeline, every component execution is logged, enabling full traceability from raw data to deployed model artifacts. This makes Pipelines the definitive tool for audit-ready lineage in production environments.Why the Other Options Are Wrong
Option A tracks dataset versions but does not capture model architecture changes or prediction lineage. Option C merely stores data without providing any metadata tracking or lineage capabilities. Option D focuses on comparing hyperparameters and metrics across multiple training runs rather than tracking production artifacts and prediction chains.Community Comment Notes
The community split heavily between B and D, reflecting a common certification dilemma. Commenters clarified that while Experiments track model versions and metrics, Pipelines are specifically designed for artifact lineage and prediction tracking (Comment #2, #3). Several users noted that the phrase 'running your custom model in production' strongly implies an orchestrated workflow, making Pipelines the correct architectural choice (Comment #10).Official Reference
Exam Strategy
When exam questions mention 'lineage,' 'artifact tracking,' or 'end-to-end workflows,' immediately select Vertex AI Pipelines over Experiments. Reserve Experiments for scenarios emphasizing hyperparameter tuning, metric comparison, or iterative model development rather than production audit trails.
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